Post on 21-Jul-2020
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A PROLOG BASED INTELLIGENT PATH FINDER1: TOWARDS AN OPEN SOURCE MANAGEMENT
SYSTEM OF A DIGITAL METROPOLIS
A.Faro, D.Giordano, C.Spampinato
Dipartimento di Ingegneria Informatica e Telecomunicazioni Facoltà di Ingegneria, Università di Catania Italy
(afaro, dgiordan, cspampin)@diit.unict.iy
1 The system will be demonstrated by an implementation carried out, with the support of G.Serri, G.Tafuri, and F.Tramontana, within the Project entitled“Catania, City of Excellence in ICT” supported by the Sicily Region and the Catania Municipality.
OVERVIEW• The General Framework: towards a digital Metropolis• Aim of the project and technology adopted• Info Mobility
– User services– management center
• Intelligent Path Finder (IPF)• Traffic Model• Monitoring System
– Traffic Human Perceptions;– Webcam Image Processing;
• IPF System Architecture• Implementation and Some use cases• Conclusions and future works
The General Framework:Digital Metropolis
z
K- GovernmentE-Culture
E-Commerce
E-Banking
BrainIntensiveServices
Physical City : People and Goods
Digital City:Bits ed Avatars
Computing IntensiveServices
InfoMobility in the large and for lastmile
Some Infrastructures of the Physical City
The General Framework:Digital Metropolis
z
K- GovernmentE-Culture
E-Commerce
E-Banking
BrainIntensiveServices
Physical City : People and Goods
Digital City:Bits ed Avatars
Computing IntensiveServices
InfoMobility in the large and for the last mile
Project and Technologies• The aim of the project is to
implement an open-sourceinfomobility system forCatania Metropolitan Area and the South-East SicilyLogistics Platform;
• A WiMax backbone, interconnecting WiFi nodes, to manage e-mobility, e-logistics and e-security byusing :– Machine Vision– Fuzzy Logic– Logic programming– GIS– Mobile Systems
Info Mobility : User Services
Server
Wimax
WimaxWimax
Geode Geode
Geode
Wi-Fi
Wi-Fi
Wi-Fi
Wi-Fi
Wi-FiWi-Fi
Wi-Fi
Wi-Fi
Wi-Fi
Stazione Wi-Max
Wi-Fi
Wi-Fi
Computer
portatile di bordo
PARKING
R-FID
Access Control and
Delivery
EMERGENCY
POLLUTION
PUBLIC TRANSPORTATION
VIDEO-SURVEILLANCE
MUSEUM
HOTEL
MINIMUM DRIVING
TIME AND PATH
NEAREST TO ME
BUSINESS
TELECOMMUTING
Info Mobility : Management Center
E-GovernanceK-Governance
EnvironmentMonitoring
Access Control
Parking Traffic Lights
TLC Center
EmergencyCivil
Protection
Municipalityand private
Fleets
TelecommutingE-Business
E-Security
E-Pollution
Traffic Lights, Intersectionsand Parking Management
VariableMessages
Public Transport.
Eco-Telematics
Fleets
Logistics
CleansingFleet
Delivery fleets and Last Mile
CleansingDepartment
InfoMobilityCenter
WiMax networkRete Radio
Smart Card
GSM/GPRS
Telecamere
RFID
Intelligent Path Finder (IPF)
This system provides, taking into account the current traffic condition, the Minimum Driving Time and Path on the metropolitannetwork for the following main services:
• to reach a destination starting from a source node specified by the user or detected by GPS;
• to pass throught a set of nodes specifiedby the user
Traffic ModelMacroscopic and microscopic approaches have been proposed to study the traffic systems:� Macroscopic � to control the traffic of an urban
network in real time;� Microscopic � off-line optimization and planning of
a limited number of intesections;
Intelligent Path Finder uses the Measurements of the main traffic parameters to provide the user with suitable traffic
forecasts derived from a macroscopic model
� The main parameters, to be measured, are the car flows (λ), density (δ) and speed(ν), related as follows:
• The traveling time (T) for each street is:
where Tinserection is the time to cross the inteserctionat the end of the street and depends on λ and δ
• To forecast the traveling time for a long period the Intelligent Path Finder uses the Origin-Destination matrix
Traffic Model : Analysis and Forecast
ν ν ν ν = λλλλ / δ δ δ δ
T = L*(λ/ δλ/ δλ/ δλ/ δ) + Tintersection (λλλλ, δ, δ, δ, δ)
Monitoring System
� Two methods are proposed to measure λ λ λ λ and δ:δ:δ:δ:� Traffic Human Perceptions based on the
Computing with words theory (L. Zadeh);� Webcam Image Processing based on the
Cellular Neural Network Processing (L.O.Chua);
Traffic Human Perceptions
To estabilish the mean velocity in the considered sectionwe have to define the memberships for the car flow (µ) and density car (δ)
MediumLow High
Density ( δδδδ)δδδδmax/5 2δ2δ2δ2δmax/5 3δ3δ3δ3δmax/5 4δ4δ4δ4δmax/5 δδδδmax
Traffic Human Perceptions
Adverbs such as few/most/quite always/ sometimes/ More or Less are useful to further qualify the perceptions
Fuzzy Engine forHuman Perceptions
Most High means:MediumLow High
δδδδmax/5 2δ2δ2δ2δmax/5 3δ3δ3δ3δmax/5 4δ4δ4δ4δmax/5 δδδδmax
Few High
Fuzzy Engine forHuman Perceptions
• To obtainthe crispvalue forthe density it’s necessaryto defuzzify(e.g., usingcentroidmethod)
δδδδ1
Few High
Fuzzy Engine forHuman Perceptions
To obtainthe crispvalue forthe density it’s necessaryto defuzzify(e.g., usingcentroidmethod)
δδδδ1
More or Less High
δδδδ2
Few High
Fuzzy Engine forHuman Perceptions
To obtainthe crispvalue forthe density it’s necessaryto defuzzify(e.g., usingcentroidmethod)
δδδδ1
More or Less High
δδδδ2
Most High
δδδδ3
Fuzzy Engine forHuman Perceptions
• Density : Most High means 85,1 cars /KmUsing 100 as max of the density;
• Flow : Most Low means 250.2 cars / hourusing 1800 as max of the flow;
• The mean velocity is given by flow/density, i.e.,:
2.94 Km/h
Traffic Human Perceptions:Web PDA Interface
Webcam Image Processing
The system developed to evaluate the traffic conditions from web-cam images consists of two parallel algorithms:– The first algorithm makes use of a small
window (coil) superimposed to the images detected by the webcams to detect the car flow;
– The second algorithm counts the number of car contained in a given window, greater than the previous one, to detect the density
Webcam Image Processing
Coil tocomputethe carflow
Webcam Image Processing
Window tocomputethe cardensity
WEBCAM TRAFFIC PROCESSING
Image Processing using Cellular neural
Network.The estimated mean
velocity is:3,66 Km/h
IPF System Architecture
Perceptions
Traffic Monitoring
City Informations
Minimum Path
Corda Server
Video sequencesProcessing
Chart withMinimum
Path
SQL database
IPF System ArchitectureUpdating the Driving Time in Real Time
Perceptions
Traffic Monitoring
Video sequencesProcessing
SQL database
The field information modifies the cost of the nodes (e.g., traveling
time)
IPF System ArchitectureMinimum Source-Destination Path
Source – Destination inserted by the user from desktop or PDA
User Desktop
PDA User
IPF Server ArchitectureMinimum Path Computing
Server
Source - Destination
Desk User
PDA User
SQL database
Prolog Engine (Traffic.exe)
interacts with the information
stored in the SQL DB
Minimum Path
All possible pathsbetween Source and
Destination
IPF Server ArchitectureMinimum Path Drawing
Minimum Path foundby the Prolog Engine
Server
Minimum PathFile Parsing
IPF Server ArchitectureMinimum Path Drawing
Corda Server
Python passes the Pathfound by the PrologEngine to the Corda
Server for visualization
Server
IPF Server ArchitectureMinimum Path Computing
Desk User
PDA User
Visualization of the Minimum Path
Real Time Minimum PathMap produced by the system supposing the userwish to go from stazione to europa under the assumption that the traffic is smoothly flowing
Real Time Minimum Path
Supposing that in the sectionRaffineria-Galatea the humanperceptions changeas follows: Flow Most LowDensity Most High
Real Time Minimum Path
The new path will be the following:
Final remarks
The main advantages of the IPF system are:
– Open Source;– Real Time;– Modular and Versatile;– Localization by GPS;– PDA and Pen-Tablet oriented;– Easy Map Manipulation (zoom, scrolling, etc..)
Future works
• Full integration of all the envisagedservices (parking, delivery, etc …) into the package;
• Extending the IPF system to manageturistic and cultural aspects;
• Testing and improvements of the forecastmodel.